Download Data Mining Solutions

Data Mining Solutions
INTRODUCTION
CAVEATS
Vast amounts of data combined with advancements in computer
processing allow Tegra to exploit computational efficiencies on
hundreds of statistical models across thousands of variables and
down millions of records. The result is data mining – the practice of
discovering patterns and trends that go beyond simple analyses to
make better decisions.
Data mining does not provide
answers. Rather, it provides
for discoveries within a large
data set to generate actionable ideas. The idea generation process
leads to new insights into the data that can then be tested. It is
often necessary to take small steps and iterate over an analysis
many times to achieve great results.
TECHNIQUES DEPLOYED
Insights gained from data can only be as robust as the sources used.
Better conclusions can be drawn from data of higher Quality and
Quantity.
Summary Tables
Trends
Correlations
Basic
Part of the success of data mining is user-driven. While technology
can be utilized to uncover hidden relationships between variables,
the practicality and value of these relationships are framed through
understanding the data and the business.
Linear regression models
Contingency tables
Logistic regression models
Nonlinear models
Splines & Smoothers
Visualizations
Discriminant analysis
Decision trees
k-NN or nearest neighbor
k-means cluster analysis
Neural networks
Monte Carlo simulations
Custom algorithms
RETURN ON DATA INVESTMENT (RODI)
Data sources, especially prescriber level data, can be expensive and
often costs hundreds of thousands per year.
Return On Data Investment (RODI) accounts for insights gained from
data mining. Tegra can do more with your data than just reporting.
Advanced
APPLICATIONS & IMPLEMENTATION
Overall knowledge discovery in databases
Data error and anomaly investigations
Physician segmentation
Physician retention
Physician probability of prescribing
Physician future trends
Physician behavior
Account clustering
Transactional patient claims data
Upselling opportunities
Territory optimization
Identify abusers
Insurance claims
Payer spillover
METHODOLOGY
Define the business problem
Build the data mining database
Prepare the data
Perform data mining analyses
Evaluate models, Train models, Validate models
Deploy model and present results
EXAMPLES
1. Duration. An exhaustive data mining exercise was applied to call
data and prescribing patterns. Tegra performed dozens of
regression models over several time periods to reveal the
importance of duration. Reach and Frequency are important. Depth
and breadth are important. However, data mining showed that
longer duration consistently resulted in higher sales performance.
2. Migration by Specialty. Data mining revealed that PCP and ENDO
prescribers behaved differently across segments. PCPs were more
likely to try the product than ENDOs, but also more likely to only try
the product once. Tegra created models to isolate segments more
worthy of sales effort resulting in substantial efficiencies for the field
sales force and faster sales growth for a newly launched product.
3. Quality Assurance Team. A combination of classic experimental
design plus data mining identified manufacturing deficiencies and
ways to optimize the quality assurance process. Using repeated
measures statistical models on machine speed, defect type, number
of defects, time of day, operator and sequence, Tegra calculated pvalues for speed levels and critical defects. In addition, data mining
models helped gain insight into inspection times, operator
variability, and false positive rates. The combined analyses resulted
in clear actionable improvements to the quality control process and
provided analytical rigor to the FDA’s requests.